U.S. patent application number 14/015420 was filed with the patent office on 2014-01-02 for systems and methods for subsurface oil recovery optimization.
The applicant listed for this patent is Landmark Graphics Corporation. Invention is credited to Gustavo Carvajal, Alvin Stanley Cullick, Hasnain Khan, Amit Kumar, Hatem Nasr, Ranjan Priyesh, Luigi Saputelli, Burt Gorell Sheldon, Karelis Alejandra Urrutia.
Application Number | 20140006111 14/015420 |
Document ID | / |
Family ID | 48044172 |
Filed Date | 2014-01-02 |
United States Patent
Application |
20140006111 |
Kind Code |
A1 |
Priyesh; Ranjan ; et
al. |
January 2, 2014 |
Systems and Methods for Subsurface Oil Recovery Optimization
Abstract
Systems and methods for subsurface secondary and/or tertiary oil
recovery optimization based on either a short term, medium term or
long term optimization analysis of selected zones, wells,
patterns/clusters and/or fields.
Inventors: |
Priyesh; Ranjan; (Houston,
TX) ; Sheldon; Burt Gorell; (Katy, TX) ;
Kumar; Amit; (Houston, TX) ; Cullick; Alvin
Stanley; (Thornton, CO) ; Carvajal; Gustavo;
(Houston, TX) ; Urrutia; Karelis Alejandra;
(Houston, TX) ; Khan; Hasnain; (Houston, TX)
; Saputelli; Luigi; (Houston, TX) ; Nasr;
Hatem; (Houston, TX) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Landmark Graphics Corporation |
Houston |
TX |
US |
|
|
Family ID: |
48044172 |
Appl. No.: |
14/015420 |
Filed: |
August 30, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14002496 |
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PCT/US12/58858 |
Oct 5, 2012 |
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14015420 |
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61544202 |
Oct 6, 2011 |
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Current U.S.
Class: |
705/7.36 |
Current CPC
Class: |
F16L 21/005 20130101;
G01B 7/023 20130101; B65F 2003/003 20130101; G06F 30/00 20200101;
E21B 43/00 20130101; F16L 55/17 20130101; E21B 43/16 20130101; G06Q
50/02 20130101; B60R 3/007 20130101; G01V 99/005 20130101; E21B
41/0092 20130101; B65F 3/00 20130101; G06F 30/20 20200101; G06Q
10/06375 20130101; G06Q 10/0637 20130101 |
Class at
Publication: |
705/7.36 |
International
Class: |
G06Q 10/06 20060101
G06Q010/06 |
Claims
1. A method for long term oil recovery optimization, which
comprises: selecting one or more zones, wells, patterns/clusters or
fields; displaying multiple optimization scenarios and
corresponding actions for optimization of the one or more selected
zones, wells, patterns/clusters or fields during an evaluation of a
plan for developing a field; selecting one or more of the
optimization scenarios and displaying each corresponding action;
selecting a prediction date for each selected optimization
scenario; and displaying the one or more selected optimization
scenarios, the effect of each corresponding action on the one or
more selected zones, wells, patterns/clusters or fields on the
prediction date, and an updated field development plan using a
computer system, the updated field development plan being displayed
for a field with a respective net present value calculation and
projected production parameters.
2. The method of claim 1, further comprising determining if
optimization is desired based on the one or more selected
optimization scenarios, the effect of each corresponding action on
the one or more selected zones, wells, patterns/clusters or fields
and the updated field development plan.
3. The method of claim 2, further comprising selecting one or more
desired optimization scenarios from the one or more selected
optimization scenarios for implementation.
4. The method of claim 3, further comprising executing the
corresponding action for each desired optimization scenario as a
long term optimization response to an undesirable sweep efficiency
health indicator.
5. The method of claim 4, wherein the corresponding action for each
desired optimization scenario is remotely executed.
6. The method of claim 3, further comprising sending an approval
for manual implementation of the corresponding action for each
desired optimization scenario.
7. The method of claim 3, further comprising sending a request for
the implementation of the corresponding action for each desired
optimization scenario with a business case report and
recommendation.
8. The method of claim 1, wherein the multiple displayed
optimization scenarios and corresponding actions are ranked
according to at least one of net present value, increased oil
recovery, and reduced recovery of unwanted gas or fluids.
9. The method of claim 8, wherein the multiple displayed
optimization scenarios and corresponding actions are ranked for
enhanced proactive optimization.
10. The method of claim 1, further comprising running the one or
more selected optimization scenarios on a simulator in real-time to
determine the effect of each corresponding action on the one or
more selected zones, wells, patterns/clusters or fields on the
prediction date.
11. The method of claim 1, further comprising diagnosing a cause of
an undesirable sweep efficiency health indicator for a sweep
efficiency health display using streamline numerical calculation to
estimate correlation factors and well allocation factors.
12. The method of claim 1, wherein the display of the one or more
selected optimization scenarios and the effect of each
corresponding action includes infill drilling and re-drilling of
water injection positions.
13. A program carrier device for carrying computer executable
instructions for long term oil recovery optimization, the
instructions being executable to implement: selecting one or more
zones, wells, patterns/clusters or fields; displaying multiple
optimization scenarios and corresponding actions for optimization
of the one or more selected zones, wells, patterns/clusters or
fields during an evaluation of a plan for developing a field;
selecting one or more of the optimization scenarios and displaying
each corresponding action; selecting a prediction date for each
selected optimization scenario; and displaying the one or more
selected optimization scenarios, the effect of each corresponding
action on the one or more selected zones, wells, patterns/clusters
or fields on the prediction date, and an updated field development
plan, the updated field development plan being displayed for a
field with a respective net present value calculation and projected
production parameters.
14. The program carrier device of claim 13, further comprising
determining if optimization is desired based on the one or more
selected optimization scenarios, the effect of each corresponding
action on the one or more selected zones, wells, patterns/clusters
or fields and the updated field development plan.
15. The program carrier device of claim 14, further comprising
selecting one or more desired optimization scenarios from the one
or more selected optimization scenarios for implementation.
16. The program carrier device of claim 15, further comprising
executing the corresponding action for each desired optimization
scenario as a long term optimization response to an undesirable
sweep efficiency health indicator.
17. The program carrier device of claim 16, wherein the
corresponding action for each desired optimization scenario is
remotely executed.
18. The program carrier device of claim 15, further comprising
sending an approval for manual implementation of the corresponding
action for each desired optimization scenario.
19. The program carrier device of claim 15, further comprising
sending a request for the implementation of the corresponding
action for each desired optimization scenario with a business case
report and recommendation.
20. The program carrier device of claim 13, wherein the multiple
displayed optimization scenarios and corresponding actions are
ranked according to at least one of net present value, increased
oil recovery, and reduced recovery of unwanted gas or fluids.
21. The program carrier device of claim 20, wherein the multiple
displayed optimization scenarios and corresponding actions are
ranked for enhanced proactive optimization.
22. The program carrier device method of claim 13, further
comprising running the one or more selected optimization scenarios
on a simulator in real-time to determine the effect of each
corresponding action on the one or more selected zones, wells,
patterns/clusters or fields on the prediction date.
23. The program carrier device of claim 13, further comprising
diagnosing a cause of an undesirable sweep efficiency health
indicator for a sweep efficiency health display using streamline
numerical calculation to estimate correlation factors and well
allocation factors.
24. The program carrier device of claim 13, wherein the display of
the one or more selected optimization scenarios and the effect of
each corresponding action includes infill drilling and re-drilling
of water injection positions.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS STATEMENT REGARDING
FEDERALLY SPONSORED RESEARCH
[0001] This application is a continuation of U.S. patent
application Ser. No. 14/002,496 filed Aug. 30, 2013, which claims
priority from PCT Patent Application No. PCT/US2012/058858, filed
on Oct. 5, 2012, which claims priority from U.S. Provisional Patent
Application No. 61/544,202, filed on Oct. 6, 2011, which are
incorporated herein by reference.
FIELD OF THE INVENTION
[0002] The present invention generally relates to systems and
methods for subsurface oil recovery optimization. More
particularly, the invention relates to subsurface secondary and/or
tertiary oil recovery optimization based on either short term,
medium term or long term optimization analysis of selected zones,
wells patterns/clusters and/or fields.
BACKGROUND OF THE INVENTION
[0003] Various systems and methods are well known for maximizing
subsurface secondary and/or tertiary oil recovery. Current systems
for maximizing secondary and/or tertiary recovery generally rely on
many steps, in different systems, and software tools, which users
need to setup and manage by themselves. This is a manual process,
where the user will create a numerical analysis model of the
reservoir, run the model with a few different operating decisions
and/or parameters, analyze the results and choose the best answer.
The unautomated process often requires running multiple
applications, which are not integrated, to obtain results to be
integrated. As a result of the different applications required, a
significant amount of reformatting data between applications may be
necessary, creating further labor and the potential for error.
Moreover, as the process is manually performed in numerous
locations, there is no electronic audit trail for later review.
This may be further complicated as analysis tools are generally
generic and not designed to integrate data and to provide and
assess simulations according to varying criteria. Current systems
provide very little feedback as to the quality of the model and
checking to make sure that the results are realistic. They do not
provide interactive graphical feedback to the user at various
levels of field operations and they do not provide true
optimization and decision support tools. They also do not leverage
the true value of real time data from the field. As a result,
current systems are manual, labor intensive, and require transfer
of data from one system to another while requiring the users to
verify that the output from one system is usable as the input to
another system. These deficiencies in current systems mean that the
number of people who can do this type of work is quite limited. As
a result, this process is performed by a limited number of experts
within an organization. With a currently available set of tools,
even these experts take a very long time to perform the process and
are prone to errors because of the manual nature of the
process.
[0004] As a result of the limitations of current systems, users
generally do not look at multiple scenarios to take into account
possible uncertainties in the underlying numerical reservoir model.
Nor to users exhaustively utilize optimization technologies to
analyze, rank and choose the best development operations to
increase secondary and/or tertiary oil recovery. This often
precludes users from addressing uncertainties in a reservoir model
by periodically reassessing selected scenarios based on data such
as historical performance of the reservoir, patterns, wells, and/or
zones or other data. Moreover, in addition to all the limitations
listed above, current systems do not provide good tools to allow a
user to update a model, or series of models. These difficulties in
generating a first model serve as a deterrent to generation of
later updates.
[0005] Nor do current systems address the overall performance of
the field or effectiveness of secondary or tertiary recovery
processes. Practitioners of the current processes will generally
recognize that sweep efficiency is an important metric of recovery
process effectiveness. Sweep efficiency can be calculated at
different locations in a field and at different scales. For
example, sweep efficiency could be calculated locally near a well,
at a zone level, between two wells, at a pattern level, at a field
level and at different levels in between. Currently, there is no
good method to measure or calculate sweep efficiency health
indicators. There is also no integrated system and method for
simultaneous simulation and optimization of well production at
different scales or ranks from the field to equipment levels.
SUMMARY OF THE INVENTION
[0006] The present invention therefore, meets the above needs and
overcomes one or more deficiencies in the prior art by providing
systems and methods for subsurface secondary and/or tertiary oil
recovery optimization based on either short term, medium term or
long term optimization analysis of selected zones, wells
patterns/clusters and/or fields.
[0007] In one embodiment, the present invention includes a method
for long term oil recovery optimization, which comprises: i)
selecting one or more zones, wells, patterns/clusters or fields;
ii) displaying multiple optimization scenarios and corresponding
actions for optimization of the one or more selected zones, wells,
patterns/clusters or fields during an evaluation of a plan for
developing a field; iii) selecting one or more of the optimization
scenarios and displaying each corresponding action; iv) selecting a
prediction date for each selected optimization scenario; and v)
displaying the one or more selected optimization scenarios, the
effect of each corresponding action on the one or more selected
zones, wells, patterns/clusters or fields on the prediction date,
and an updated field development plan using a computer system, the
updated field development plan being displayed for a field with a
respective net present value calculation and projected production
parameters.
[0008] In another embodiment, the present invention includes a
program carrier device for carrying computer executable
instructions for long term oil recovery optimization. The
instructions are executable to implement: i) selecting one or more
zones, wells, patterns/clusters or fields; ii) displaying multiple
optimization scenarios and corresponding actions for optimization
of the one or more selected zones, wells, patterns/clusters or
fields during an evaluation of a plan for developing a field; iii)
selecting one or more of the optimization scenarios and displaying
each corresponding action; iv) selecting a prediction date for each
selected optimization scenario; and v) displaying the one or more
selected optimization scenarios, the effect of each corresponding
action on the one or more selected zones, wells, patterns/clusters
or fields on the prediction date, and an updated field development
plan, the updated field development plan being displayed for a
field with a respective net present value calculation and projected
production parameters.
[0009] Additional aspects, advantages and embodiments of the
invention will become apparent to those skilled in the art from the
following description of the various embodiments and related
drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] The present invention is described below with references to
the accompanying drawings in which like elements are referenced
with like reference numerals, and in which:
[0011] FIG. 1 illustrates an overall process for subsurface oil
recovery optimization according to the present invention.
[0012] FIG. 2 is a flow diagram illustrating one embodiment of a
method for implementing the present invention.
[0013] FIG. 3 is a flow diagram illustrating one embodiment of a
method for performing step 216 in FIG. 2.
[0014] FIG. 4 is a flow diagram illustrating one embodiment of a
method for performing step 220 in FIG. 2.
[0015] FIG. 5 is a flow diagram illustrating one embodiment of a
method for performing step 224 in FIG. 2.
[0016] FIG. 6 is a block diagram illustrating one embodiment of a
system for implementing the present invention.
[0017] FIG. 7 is an exemplary graphical user interface illustrating
step 204 in FIG. 2.
[0018] FIG. 8 is an exemplary graphical user interface illustrating
step 206 in FIG. 2.
[0019] FIG. 9 is an exemplary graphical user interface illustrating
step 306 in FIG. 3.
[0020] FIG. 10 is an exemplary graphical user interface
illustrating step 324 in FIG. 3.
[0021] FIG. 11 is an exemplary graphical user interface
illustrating step 406 in FIG. 4.
[0022] FIG. 12 is an exemplary graphical user interface
illustrating step 412 in FIG. 4.
[0023] FIG. 13 is an alternative exemplary graphical user interface
illustrating step 412 in FIG. 4.
[0024] FIG. 14 is an exemplary graphical user interface
illustrating step 422 in FIG. 4.
[0025] FIG. 15 is a table illustrating exemplary levels of
optimization provided by the present invention.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0026] The subject matter of the present invention is described
with specificity, however, the description itself is not intended
to limit the scope of the invention. The subject matter thus, might
also be embodied in other ways, to include different steps or
combinations of steps similar to the ones described herein, in
conjunction with other present or future technologies. Moreover,
although the term "step" may be used herein to describe different
elements of methods employed, the term should not be interpreted as
implying any particular order among or between various steps herein
disclosed unless otherwise expressly limited by the description to
a particular order. While the following description refers to the
oil and gas industry, the systems and methods of the present
invention are not limited thereto and may also be applied in other
industries to achieve similar results.
[0027] The present invention includes systems and methods for
optimizing oil recovery, by reducing unwanted fluid/gas production,
reducing workover downtime, reducing by-passed oil and gas, and
maximizing net present value through optimization of injection and
production profiles. The systems and methods therefore, consider
intelligent manipulation of subsurface displacement profiles;
surface and facility optimization constraints, well
intervention/recompletion designs, and dynamic field development
planning through decisions to drill and design new
producer/injector/observation wells.
[0028] The systems and methods perform all permutations and
combinations with surveillance, diagnostics and optimization from a
micro to a macro scale spanning from the equipment level to the
zone level, to well level to a pattern/cluster level to, finally,
the reservoir/field level. The systems and methods allow the user
to perform present and/or predictive diagnostics on the field
and/or sweep efficiency health, as well as advise the user of
optimum optimization actions for short, medium and long term time
frames. The systems and methods allow the user to interactively
perform comparative "what if" scenarios (war games) with the
previously advised optimization actions, generate appropriate
business cases and thus, take and implement the appropriate
optimization actions that help maximize oil recovery and economic
value.
[0029] The systems and methods utilize real-time surveillance field
data to provide advanced value of integrated asset management,
which provides an automated advisory for short, medium and/or long
term multiple-well/pattern and field level optimization. The
systems and methods allow personnel to perform predictive analysis
on the effect of selected optimization actions, and deliver an
intuitive user interface for enhanced collaborative decision making
between asset, reservoir, operations, and production personnel. The
systems and methods, therefore, obviate the need for labor
intensive simulation and optimization in separate actions.
[0030] In short, the systems and methods enable monitoring of the
subsurface health of a production field and provide automated
advisory on proactive reservoir diagnostics with tangible
optimization actions, thus permitting forecasted analysis on the
proposed reservoir optimization actions.
Method Description
[0031] Referring now to FIG. 1, an overall process 100 for
subsurface oil recovery according to the present invention is
illustrated.
[0032] In step 102, the process 100 identifies present field
health. One embodiment of a method for identifying field health
today is illustrated by step 202 in FIG. 2.
[0033] In step 104, the process 100 predicts field health. One
embodiment of a method for field health prediction is illustrated
by steps 204-208 in FIG. 2.
[0034] In step 106, the process 100 diagnoses field health for
today and the future, which may include identifying and detecting
the bypassed and unswept oil spots using a mobile water saturation
function. One embodiment of a method for diagnosing field health
for today and the future is illustrated by step 210 in FIG. 2.
[0035] In step 108, the process 100 advises optimization for short,
medium, and long terms, if optimization is desired. One embodiment
of a method for determining the desired optimization is illustrated
by steps 212, 214, 218, and 222 in FIG. 2.
[0036] If optimization is desired, then the user must also select
whether the time-frame for optimization will be short term, medium
term, or long term. If short term optimization is desired, then one
embodiment of a method for short term optimization is illustrated
by steps 302-306 in FIG. 3. If medium term optimization is desired,
then one embodiment of a method for medium term optimization is
illustrated by steps 402-406 in FIG. 4. If long term optimization
is desired, then one embodiment of a method for long term
optimization is illustrated by steps 502-506 in FIG. 5.
[0037] Optimization may be provided as an automated advisory for
reactive and proactive optimization of sweep efficiency to achieve
key performance targets--including time horizons (from 1 day to any
number of years), reducing water handling (as a percentage),
reducing downtime for workover times (as a percentage), reducing
by-passed oil, and increasing recovery from new wells and
recompletions (as a percentage). Optimization may also enable
timely decisions based on real-time data to provide updated,
predictive models and provide expert system and optimized
advisories.
[0038] In step 110, the process 100 includes "what if" scenarios to
assess and compare various optimization scenarios, which may also
be regarded as optimization war games. One embodiment of a method
for conducting optimization "what if" scenarios is illustrated by
steps 308-316 in FIG. 3 for short term optimization, steps 408-416
in FIG. 4 for medium term optimization, and steps 508-516 in FIG. 5
for long term optimization.
[0039] In step 112, the process 110 implements the optimization.
One embodiment of a method for obtaining or seeking optimization
implementation is illustrated by steps 318-326 in FIG. 3 for short
term optimization, steps 418-426 in FIG. 4 for medium term
optimization, and steps 518-526 in FIG. 5 for long term
optimization.
[0040] The overall process 100 therefore, provides a fully
integrated subsurface reservoir management solution for improving
sweep efficiency and allowing reservoir and production personnel
(likely engineers) to collaborate. This may be accomplished while
monitoring reservoir dynamics during production, utilizing surface
and downhole sensors, updating and simulating the reservoir and
well models. This may provide control strategies for
short-production optimization and increased recovery utilizing
surface chokes, ICD's and smartwells while implementing
optimization strategies on future planning, such as infill drilling
to recover bypassed oil.
[0041] The process 100 for optimization may be reactive, simple
proactive, or enhanced proactive ("proactive plus"). Reactive
optimization may be characterized as an immediate reaction to
current conditions. Reactive optimization may occur in the short
term and may be directed to actions such as optimizing choke
settings and production/injection rates. Simple proactive
optimization may be characterized as an action based on predicted
conditions, such as to predict fluid movement away from the
wellbore and therefore, to optimize subsurface operations by taking
measures such as choking a downhole valve setting in order to
increase total recovery. Simple proactive optimization also focuses
on long term field development planning optimization such as
scheduling future infill drilling producer/injector locations,
workovers, their configurations, etc. Enhanced proactive
optimization, on the other hand, provides for right time
integration of exploration, drilling, completion and production
disciplines while evaluating the appropriate plan of action for
developing a field to ensure there is sufficient time after
optimization options are identified that might effect them. Simple
proactive optimization may occur over the medium term to long term
(such as, but not limited to, three months to 2 years) and include
the actions of reactive optimization together with short term to
medium term field development plan updates. Thus, integration
involves running several reservoir depletion scenarios as well as
cost/benefit analysis scenarios, in real time, thus helping plan
the best integrated solution across all disciplines of an asset
development life cycle. Enhanced proactive optimization for
example, could allow the operator to change completion and
production planning in real time for better ultimate depletion,
while actually drilling and gathering additional information about
the reservoir. The goals of each of these exemplary levels of
optimization are illustrated by the table 1500 in FIG. 15.
[0042] Thus, the process 100 depends on right time reservoir
management including continuous reservoir visualization, proactive
reservoir diagnostics and optimization, and predictive reservoir
optimization analysis.
[0043] Referring now to FIG. 2, a flow diagram illustrates one
embodiment of a method 200 for implementing the present
invention.
[0044] In step 201, current conditions data or previously computed
scenario conditions data are selected using the client interface
and/or the video interface described in reference to FIG. 6.
Selection of whether to use current conditions data or previously
computed scenario conditions data may be based on a subjective
determination of whether to use current conditions or previous
optimizations. Current conditions data provides the ability to
assess the present health of the field and to perform optimizations
based on that data. Previously computed scenario conditions data
provides the ability to review the past health of the field in
relation to current health and to perform optimizations based on
saved data, which may include optimized short, medium or even long
term data.
[0045] In step 202, the current sweep efficiency health is
displayed using techniques well known in the art and the video
interface described in reference to FIG. 6. Subsurface
visualization techniques and current sweep efficiency health
indicators, for example, may be used with integrated current
conditions data, previously computed scenario conditions data and
historic data to provide a display of ranked zones, wells,
patterns/sectors and/or fields representing the current sweep
efficiency health. Effective subsurface visualization requires
visualization of the reservoir dynamics as the subsurface changes
in the wellbore, near the wellbore and away from the wellbore. A
goal of subsurface visualization is to create a very high
resolution three dimensional (3-D) visualization interface, which
may include various features including fiber optic monitoring
visualization, surface deformation visualization, 3D fluid
displacement visualization, bypassed oil 3D visualization,
oil/water interface visualization, streamlines visualization,
field/zone/well maps, isobaric maps, saturation maps, injection
patterns at subsurface zone-levels, and zone level allocations of
production/injection.
[0046] In step 204, a future date for the prediction of sweep
efficiency health without optimization and the number of
intervening periods are selected using the client interface and/or
the video interface described in reference to FIG. 6. Selection of
the future date and intervening periods is subjective and is based
on the preference and/or experience of the user. One example of a
future date selected for the prediction of sweep efficiency health
without optimization and the number of intervening periods is
illustrated by the graphical user interface 700 in FIG. 7, which
illustrates a future date four (4) years in the future and
intervening periods of one year.
[0047] In step 206, displays of the predicted sweep efficiency
health at the selected future date and at the end of each of the
intervening periods are generated using techniques well known in
the art and the video interface described in reference to FIG. 6.
The displays include a rank of the sweep efficiency health for the
identified zones, wells, patterns/sectors and/or fields as well as
other potential user-defined spatial scales. One example of a
display of the predicted sweep efficiency health at a selected
future date and at the end of each intervening period selected for
FIG. 7 is illustrated by the graphical user interface 800 in FIG.
8.
[0048] In step 208, one of the displays of the predicted sweep
efficiency health or the display of the current sweep efficiency
health is selected using the client interface and/or the video
interface described in reference to FIG. 6. Each selected display
may provide further detail, including the history for sweep
efficiency health indicators at any scale of the zone, well,
pattern/sector and/or field.
[0049] In step 210, the cause of any undesirable sweep efficiency
health indicators for the selected sweep efficiency health display
is diagnosed using well known diagnostic techniques, such as those
found in the DecisionSpace.TM. software for reservoir simulation.
The cause may be displayed by an automated advisory feature that
utilizes indicators including volumetric efficiency, voidage
replacement, displacement efficiency, nominal pressure and wellbore
capture factor (Fcap) layer by layer in the reservoir. The cause
can also be diagnosed by comparing current conditions data with
historic data or previously computed scenario conditions data.
Various diagnostics can also be performed by evaluating a flow or
production index that is normalized by a length of the perforating
interval. A streamline numerical calculation can also be used to
estimate correlation factors and well allocation factors.
[0050] In step 212, the method 200 determines whether optimization
analysis of production is desired based on the results of step 210.
If optimization analysis is desired, then the method 200 proceeds
to step 214. Alternatively, the method 200 may proceed to steps 218
or 222 if optimization analysis is desired. Optimization analysis
may be desired, for example, if the cause of any undesirable sweep
efficiency health indicators is identified by the diagnostic
performed in step 210. Otherwise, optimization analysis may not be
desired if there are no undesirable sweep efficiency health
indicators. If optimization analysis is not desired, then the
method 200 ends.
[0051] In step 214, the method 200 determines if short term
optimization analysis is desired based on the results of step 210
and whether the cause of any undesirable sweep efficiency health
indicators can be immediately resolved (e.g. by adjusting a choke).
If short term optimization analysis is not desired, then the method
200 proceeds to step 218. Alternatively, the method 200 may proceed
to step 222 if short term optimization analysis is not desired. If
short term optimization analysis is desired, then the method 200
proceeds to step 216.
[0052] In step 216, short term optimization is performed. One
embodiment of a method for performing short term optimization is
illustrated in FIG. 3.
[0053] In step 218, the method 200 determines if medium term
optimization analysis is desired based on the results of step 210
and whether the cause of any undesirable sweep efficiency health
indicators cannot be resolved immediately but may be resolved
within a matter of a day up to a few months (e.g. equipment
repair). If medium term optimization analysis is not desired, then
the method 200 proceeds to step 222. Alternatively, the method 200
may proceed to step 214 if medium term optimization analysis is not
desired. If medium term optimization analysis is desired, then the
method 200 proceeds to step 220.
[0054] In step 220, medium term optimization is performed. One
embodiment of a method for performing medium term optimization is
illustrated in FIG. 4.
[0055] In step 222, the method 200 determines if long term
optimization analysis is desired based on the results of step 210
and whether the cause of any undesirable sweep efficiency health
indicators cannot be resolved immediately or in a few months but
may be resolved within a year or longer (e.g. drilling new wells).
The decision between performing short term optimization analysis,
medium term optimization analysis or long term optimization
analysis is subjectively based on the experiences and expertise of
the person making the decision. If long term optimization analysis
is not desired, then the method 200 ends. Alternatively, the method
200 may proceed to step 214 or step 218 if long term optimization
analysis is not desired. If long term optimization analysis is
desired, then the method 200 proceeds to step 224.
[0056] In step 224, long term optimization is performed. One
embodiment of a method for performing long term optimization is
illustrated in FIG. 5.
[0057] Referring now to FIG. 3, a flow diagram illustrates one
embodiment of a method 300 for performing step 216 in FIG. 2.
[0058] In step 302, all zones, wells, patterns/clusters and/or
fields to be optimized are selected from the selected sweep
efficiency health display using the client interface and/or the
video interface described in reference to FIG. 6.
[0059] In step 304, a series of ranked optimization scenarios and
corresponding actions for reactive optimization are displayed using
the video interface described in reference to FIG. 6 and techniques
well known in the art. The series of ranked optimization scenarios
and corresponding actions for reactive optimization are based on
the optimization of the selected zones, wells, patterns, clusters
and/or fields, which may be exported to a net present value
calculator. Thousands of optimization scenarios can be created by
reservoir simulation or utilizing proxy models.
[0060] In step 306, one or more optimization scenarios may be
selected and the corresponding action for the optimization of the
selected zones, wells, patterns/clusters and/or fields is displayed
using the client interface and/or the video interface described in
reference to FIG. 6. One example of a display of the corresponding
action is illustrated by the graphical user interface 900 in FIG.
9.
[0061] In step 310, a prediction date for each selected
optimization scenario may be selected using the client interface
and/or the video interface described in reference to FIG. 6. The
prediction date determines the period of time each respective
selected optimization scenario is run on a simulator.
[0062] In step 312, the one or more selected optimization scenarios
and the effect of each corresponding action on the selected zones,
wells, patterns/clusters and/or fields on the prediction date is
displayed using the video interface described in reference to FIG.
6. The display may include, for example, changes in sweep
efficiency health indicators, various subsurface visualization
parameters for the selected zones, wells, patterns/clusters and/or
fields, and various net present value derivatives for each selected
optimization scenario.
[0063] In step 314, the method 300 determines whether optimization
is desired based on the results of step 312. If optimization is
desired, then the method 300 proceeds to step 316. If optimization
is not desired, then the method 300 proceeds to step 318.
[0064] In step 316, the desired optimization scenario(s) may be
selected from the one or more selected optimization scenarios for
implementation using the client interface and/or the video
interface described in reference to FIG. 6.
[0065] In step 318, the data underlying the results of step 312 is
saved.
[0066] In step 320, the data underlying the results of step 312
selected in step 316 for implementation is saved.
[0067] In step 322, the method 300 determines whether the user has
action approval to unilaterally implement the desired optimization
scenario(s). If the user does not have the action approval, then
the method 300 proceeds to step 324. If the user has action
approval, then the method 300 proceeds to step 326.
[0068] In step 324, a request for implementation of the desired
optimization scenario(s) may be generated and/or sent with a
business case report, recommendation and analysis using the client
interface and/or the video interface described in reference to FIG.
6. One example of a request for implementation of the desired
application scenario(s) is illustrated by the graphical user
interface 1000 in FIG. 10.
[0069] In step 326, the corresponding action(s) for each desired
optimization scenario to be implemented may be remotely executed or
approved for manual implementation using the client interface
and/or the video interface described in reference to FIG. 6.
[0070] Referring now to FIG. 4, a flow diagram illustrates one
embodiment of a method 400 for performing step 220 in FIG. 2.
[0071] In step 402, all zones, wells, patterns/clusters and/or
fields to be optimized are selected from the selected sweep
efficiency health display using the client interface and/or the
video interface described in reference to FIG. 6.
[0072] In step 404, a series of ranked optimization scenarios and
corresponding actions for proactive optimization are displayed
using the video interface described in reference to FIG. 6 and
techniques well known in the art. The series of ranked optimization
scenarios and corresponding actions for proactive optimization are
based on the optimization of the selected zones, wells, patterns,
clusters and/or fields, which may be exported to a net present
value calculator. The optimization actions could be actions such as
workovers/recompletions, conformance, surface instrumentation and
others.
[0073] In step 406, one or more optimization scenarios may be
selected and the corresponding action for the optimization of the
selected zones, wells, patterns/clusters and/or fields is displayed
using the client interface and/or the video interface described in
reference to FIG. 6. One example of selecting one or more
optimization scenarios is illustrated by the graphical user
interface 1100 in FIG. 11.
[0074] In step 410, a prediction date for each selected
optimization scenario may be selected using the client interface
and/or the video interface described in reference to FIG. 6. The
prediction date determines the period of time each respective
selected optimization scenario is run on a simulator.
[0075] In step 412, the one or more selected optimization
scenarios, the effect of each corresponding action on the selected
zones, wells, patterns/clusters and/or fields on the prediction
date, and an updated field development plan for the field with the
respective net present value calculation and projected production
parameters are displayed using the video interface described in
reference to FIG. 6. The display may include, for example, changes
in sweep efficiency health indicators, various subsurface
visualization parameters for the selected zones, wells,
patterns/clusters and/or fields, and various net present value
derivatives for each selected optimization scenario. One example of
such a display is illustrated by the graphical user interface 1200
and 1300 in FIGS. 12 and 13, respectively.
[0076] In step 414, the method 400 determines whether optimization
is desired based on the results of step 412. If optimization is
desired, then the method 400 proceeds to step 416. If optimization
is not desired, then the method 400 proceeds to step 418.
[0077] In step 416, the desired optimization scenario(s) may be
selected from the one or more selected optimization scenarios for
implementation using the client interface and/or the video
interface described in reference to FIG. 6.
[0078] In step 418, the data underlying the results of step 412 is
saved.
[0079] In step 420, the data underlying the results of step 412
selected in step 416 for implementation is saved.
[0080] In step 422, the method 400 determines whether the user has
action approval to unilaterally implement the desired optimization
scenario(s). If the user does not have the action approval, then
the method 400 proceeds to step 424. If the user has action
approval, then the method 400 proceeds to step 426. One example of
action approval to implement the desired optimization scenarios) is
illustrated by the graphical user interface 1400 in FIG. 14.
[0081] In step 424, a request for implementation of the desired
optimization scenario(s) may be generated and/or sent with a
business case report, recommendation and analysis using the client
interface and/or the video interface described in reference to FIG.
6.
[0082] In step 426, the corresponding action(s) for each desired
optimization scenario to be implemented may be remotely executed or
approved for manual implementation using the client interface
and/or the video interface described in reference to FIG. 6.
[0083] Referring now to FIG. 5, a flow diagram illustrates one
embodiment of a method 500 for performing step 224 in FIG. 2.
[0084] In step 502, all zones, wells, patterns/clusters and/or
fields to be optimized are selected from the selected sweep
efficiency health display using the client interface and/or the
video interface described in reference to FIG. 6.
[0085] In step 504, a series of ranked optimization scenarios and
corresponding actions derived from right time (the desired future
point in time--short, medium or long term) integration of
exploration, drilling, completion and production disciplines for
enhanced proactive (proactive plus) optimization are displayed
using the video interface described in reference to FIG. 6 and
techniques well known in the art while evaluating the appropriate
plan of action for developing a field. The series of ranked
optimization scenarios and corresponding actions for proactive plus
optimization are based on the optimization of the selected zones,
wells, patterns, clusters and/or fields, which may be exported to a
net present value calculator.
[0086] In step 506, one or more optimization scenarios may be
selected and the corresponding action for the optimization of the
selected zones, wells, patterns/clusters and/or fields is displayed
using the client interface and/or the video interface described in
reference to FIG. 6.
[0087] In step 510, a prediction date for each selected
optimization scenario may be selected using the client interface
and/or the video interface described in reference to FIG. 6. The
prediction date determines the period of time each respective
selected optimization scenario is run on a simulator.
[0088] In step 512, the one or more selected optimization
scenarios, the effect of each corresponding action on the selected
zones, wells, patterns/clusters and/or fields on the prediction
date, and an updated field development plan for the field with the
respective net present value calculation and projected production
parameters are displayed using the video interface described in
reference to FIG. 6. The display may include, for example, changes
in sweep efficiency health indicators, various subsurface
visualization parameters for the selected zones, wells,
patterns/clusters and/or fields, and various net present value
derivatives for each selected optimization scenario. The
optimization scenarios could include actions such as long term
exploration strategies of secondary and tertiary oil recovery,
infill drilling, re-drilling of water injection positions, and
other field development actions.
[0089] In step 514, the method 500 determines whether optimization
is desired based on the results of step 512. If optimization is
desired, then the method 500 proceeds to step 516. If optimization
is not desired, then the method 500 proceeds to step 518.
[0090] In step 516, the desired optimization scenario(s) may be
selected from the one or more selected optimization scenarios for
implementation using the client interface and/or the video
interface described in reference to FIG. 6.
[0091] In step 518, the data underlying the results of step 512 is
saved.
[0092] In step 520, the data underlying the results of step 512
selected in step 516 for implementation is saved.
[0093] In step 522, the method 500 determines whether the user has
action approval to unilaterally implement the desired optimization
scenario(s). If the user does not have the action approval, then
the method 500 proceeds to step 524. If the user has action
approval, then the method 500 proceeds to step 526.
[0094] In step 524, a request for implementation of the desired
optimization scenario(s) may be generated and/or sent with a
business case report, recommendation and analysis using the client
interface and/or the video interface described in reference to FIG.
6.
[0095] In step 526, the corresponding action(s) for each desired
optimization scenario to be implemented may be remotely executed or
approved for manual implementation using the client interface
and/or the video interface described in reference to FIG. 6.
System Description
[0096] The present invention may be implemented through a
computer-executable program of instructions, such as program
modules, generally referred to software applications or application
programs executed by a computer. The software may include, for
example, routines, programs, objects, components, and data
structures that perform particular tasks or implement particular
abstract data types. The software forms an interface to allow a
computer to react according to a source of input.
DecisionSpace.TM., which is a commercial software application
marketed by Landmark Graphics Corporation, may be used as an
interface application to implement the present invention. The
software may also cooperate with other code segments to initiate a
variety of tasks in response to data received in conjunction with
the source of the received data. Other code segments may provide
optimization components including, but not limited to, neural
networks, earth modeling, history matching, optimization,
visualization, data management, reservoir simulation and economics.
The software may be stored and/or carried on any variety of memory
such as CD-ROM, magnetic disk, bubble memory and semiconductor
memory (e.g., various types of RAM or ROM). Furthermore, the
software and its results may be transmitted over a variety of
carrier media such as optical fiber, metallic wire, and/or through
any of a variety of networks, such as the Internet.
[0097] Moreover, those skilled in the art will appreciate that the
invention may be practiced with a variety of computer-system
configurations, including hand-held devices, multiprocessor
systems, microprocessor-based or programmable-consumer electronics,
minicomputers, mainframe computers, and the like. Any number of
computer-systems and computer networks are acceptable for use with
the present invention. The invention may be practiced in
distributed-computing environments where tasks are performed by
remote-processing devices that are linked through a communications
network. In a distributed-computing environment, program modules
may be located in both local and remote computer-storage media
including memory storage devices. The present invention may
therefore, be implemented in connection with various hardware,
software or a combination thereof, in a computer system or other
processing system.
[0098] Referring now to FIG. 6, a block diagram illustrates one
embodiment of a system for implementing the present invention on a
computer. The system includes a computing unit, sometimes referred
to as a computing system, which contains memory, application
programs, a client interface, a video interface, and a processing
unit. The computing unit is only one example of a suitable
computing environment and is not intended to suggest any limitation
as to the scope of use or functionality of the invention.
[0099] The memory primarily stores the application programs, which
may also be described as program modules containing
computer-executable instructions, executed by the computing unit
for implementing the present invention described herein and
illustrated in FIG. 2. The memory therefore, includes a subsurface
oil recovery optimization module, which enables the methods
illustrated and described in reference to FIG. 2 and integrates
functionality from the remaining application programs illustrated
in FIG. 6. The subsurface oil recovery optimization module, for
example, may be used, unlike the prior art, to execute many of the
functions described in reference to steps 201, 202, 204, 206 (as to
display), 208, 210, 212, 214, 218, 222, 302, 304 (as to display and
ranking), 306 (user selection), 308 (as to selection), 310, 312 (as
to display), 314, 316, 318, 320, 322, 324, 326, 402, 404 (as to
display and ranking), 406 (user selection), 408 (as to selection),
410, 412 (as to display), 414, 416, 418, 420, 422, 424, 426, 502,
504 (as to display and ranking), 506 (user selection), 508 (as to
selection), 510, 512 (as to display), and 514, 516, 518, 520, 522,
524, 526 in FIGS. 2, 3, 4 and 5. The memory also includes
DecisionSpace.TM., which may be used, for example, as an interface
application to execute the functions described in reference to
steps 206 (as to predicted sweep efficiency health), 304 (as the
computation of ranked scenarios), 306 (advised actions), 308 (as to
effects), 312 (as to predicted changes in sweep efficiency health
indicators), 404 (as the computation of ranked scenarios), 406
(advised actions), 408 (as to effects), 412 (as to predicted
changes in sweep efficiency health indicators), 504 (as the
computation of ranked scenarios), 506 (advised actions), 508 (as to
effects), and 512 (as to predicted changes in sweep efficiency
health indicators) in FIGS. 2, 3, 4 and 5. Although
DecisionSpace.TM. may be used as an interface application, other
interface applications may be used, instead, or the subsurface oil
recovery optimization module may be used as a stand-alone
application.
[0100] Although the computing unit is shown as having a generalized
memory, the computing unit typically includes a variety of computer
readable media. By way of example, and not limitation, computer
readable media may comprise computer storage media and
communication media. The computing system memory may include
computer storage media in the form of volatile and/or nonvolatile
memory such as a read only memory (ROM) and random access memory
(RAM). A basic input/output system (BIOS), containing the basic
routines that help to transfer information between elements within
the computing unit, such as during start-up, is typically stored in
ROM. The RAM typically contains data and/or program modules that
are immediately accessible to and/or presently being operated on by
the processing unit. By way of example, and not limitation, the
computing unit includes an operating system, application programs,
other program modules, and program data.
[0101] The components shown in the memory may also be included in
other removable/non-removable, volatile/nonvolatile computer
storage media or they may be implemented in the computing unit
through an application program interface ("API") or cloud
computing, which may reside on a separate computing unit connected
through a computer system or network. For example only, a hard disk
drive may read from or write to non-removable, nonvolatile magnetic
media, a magnetic disk drive may read from or write to a removable,
nonvolatile magnetic disk, and an optical disk drive may read from
or write to a removable, nonvolatile optical disk such as a CD ROM
or other optical media. Other removable/non-removable,
volatile/nonvolatile computer storage media that can be used in the
exemplary operating environment may include, but are not limited
to, magnetic tape cassettes, flash memory cards, digital versatile
disks, digital video tape, solid state RAM, solid state ROM, and
the like. The drives and their associated computer storage media
discussed above provide storage of computer readable instructions,
data structures, program modules and other data for the computing
unit.
[0102] A client may enter commands and information into the
computing unit through the client interface, which may be input
devices such as a keyboard and pointing device, commonly referred
to as a mouse, trackball or touch pad. Input devices may include a
microphone, joystick, satellite dish, scanner, voice recognition or
gesture recognition, or the like. These and other input devices are
often connected to the processing unit through a system bus, but
may be connected by other interface and bus structures, such as a
parallel port or a universal serial bus (USB).
[0103] A monitor or other type of display device may be connected
to the system bus via an interface, such as a video interface. A
graphical user interface ("GUI") may also be used with the video
interface to receive instructions from the client interface and
transmit instructions to the processing unit. In addition to the
monitor, computers may also include other peripheral output devices
such as speakers and printer, which may be connected through an
output peripheral interface.
[0104] Although many other internal components of the computing
unit are not shown, those of ordinary skill in the art will
appreciate that such components and their interconnection are well
known.
[0105] While the present invention has been described in connection
with presently preferred embodiments, it will be understood by
those skilled in the art that it is not intended to limit the
invention to those embodiments. It is therefore, contemplated that
various alternative embodiments and modifications may be made to
the disclosed embodiments without departing from the spirit and
scope of the invention defined by the appended claims and
equivalents thereof.
* * * * *